# Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

arXiv: https://arxiv.org/abs/1903.03714 · HTML: https://mlsanity.com/paper/1903.03714/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2019.

## Built on by (most cited first, 30 of 40)

- [Learning Intents behind Interactions with Knowledge Graph for Recommendation](https://mlsanity.com/paper/2102.07057/) (2021)
- [Trustworthy AI: A Computational Perspective](https://mlsanity.com/paper/2107.06641/) (2021)
- [When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities](https://mlsanity.com/paper/2307.16376/) (2023)
- [Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices](https://mlsanity.com/paper/2110.13475/) (2021)
- [Multi-Behavior Sequential Recommendation with Temporal Graph Transformer](https://mlsanity.com/paper/2206.02687/) (2022)
- [DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation](https://mlsanity.com/paper/2206.10848/) (2022)
- [RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation](https://mlsanity.com/paper/2101.12457/) (2021)
- [Explainable Machine Learning with Prior Knowledge: An Overview](https://mlsanity.com/paper/2105.10172/) (2021)
- [Efficient Non-Sampling Knowledge Graph Embedding](https://mlsanity.com/paper/2104.10796/) (2021)
- [Recent Developments in Recommender Systems: A Survey](https://mlsanity.com/paper/2306.12680/) (2023)
- [Recent Advances in Heterogeneous Relation Learning for Recommendation](https://mlsanity.com/paper/2110.03455/) (2021)
- [LLM-Powered Explanations: Unraveling Recommendations Through Subgraph Reasoning](https://mlsanity.com/paper/2406.15859/) (2024)
- [FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings](https://mlsanity.com/paper/2205.00313/) (2022)
- [AGI for Agriculture](https://mlsanity.com/paper/2304.06136/) (2023)
- [Comprehensible Artificial Intelligence on Knowledge Graphs: A survey](https://mlsanity.com/paper/2404.03499/) (2024)
- [CoVA: Context-aware Visual Attention for Webpage Information Extraction](https://mlsanity.com/paper/2110.12320/) (2021)
- [LANE: Logic Alignment of Non-tuning Large Language Models and Online Recommendation Systems for Explainable Reason Generation](https://mlsanity.com/paper/2407.02833/) (2024)
- [Graph-based Extractive Explainer for Recommendations](https://mlsanity.com/paper/2202.09730/) (2022)
- [A Case Study for Compliance as Code with Graphs and Language Models: Public release of the Regulatory Knowledge Graph](https://mlsanity.com/paper/2302.01842/) (2023)
- [Knowledge-grounded Natural Language Recommendation Explanation](https://mlsanity.com/paper/2308.15813/) (2023)
- [RecKG: Knowledge Graph for Recommender Systems](https://mlsanity.com/paper/2501.03598/) (2025)
- [MQuinE: a cure for "Z-paradox" in knowledge graph embedding models](https://mlsanity.com/paper/2402.03583/) (2024)
- [Learning Entity Linking Features for Emerging Entities](https://mlsanity.com/paper/2208.03877/) (2022)
- [Knowledge is Power, Understanding is Impact: Utility and Beyond Goals, Explanation Quality, and Fairness in Path Reasoning Recommendation](https://mlsanity.com/paper/2301.05944/) (2023)
- [Explainable Session-based Recommendation via Path Reasoning](https://mlsanity.com/paper/2403.00832/) (2024)
- [Topic-Aware Knowledge Graph with Large Language Models for Interoperability in Recommender Systems](https://mlsanity.com/paper/2412.20163/) (2024)
- [KGIF: Optimizing Relation-Aware Recommendations with Knowledge Graph Information Fusion](https://mlsanity.com/paper/2501.04161/) (2025)
- [Rating and aspect-based opinion graph embeddings for explainable recommendations](https://mlsanity.com/paper/2107.03385/) (2021)
- [Towards Explainable Artificial Intelligence in Banking and Financial Services](https://mlsanity.com/paper/2112.08441/) (2021)
- [KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?](https://mlsanity.com/paper/2404.03164/) (2024)

## Abstract (arXiv)

Explainability and effectiveness are two key aspects for building recommender systems. Prior efforts mostly focus on incorporating side information to achieve better recommendation performance. However, these methods have some weaknesses: (1) prediction of neural network-based embedding methods are hard to explain and debug; (2) symbolic, graph-based approaches (e.g., meta path-based models) require manual efforts and domain knowledge to define patterns and rules, and ignore the item association types (e.g. substitutable and complementary). In this paper, we propose a novel joint learning framework to integrate textitinduction of explainable rules from knowledge graph with textitconstruction of a rule-guided neural recommendation model. The framework encourages two modules to complement each other in generating effective and explainable recommendation: 1) inductive rules, mined from item-centric knowledge graphs, summarize common multi-hop relational patterns for inferring different item associations and provide human-readable explanation for model prediction; 2) recommendation module can be augmented by induced rules and thus have better generalization ability dealing with the cold-start issue. Extensive experimentsfootnoteCode and data can be found at: urlhttps://github.com/THUIR/RuleRec show that our proposed method has achieved significant improvements in item recommendation over baselines on real-world datasets. Our model demonstrates robust performance over "noisy" item knowledge graphs, generated by linking item names to related entities.